diff --git a/Sentiment_Research_Exploration.py b/Sentiment_Research_Exploration.py
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+import marimo
+
+__generated_with = "0.18.0"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import marimo as mo
+    return (mo,)
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    # Sentiment & Thematic Analysis of Interviews Using LLMs
+
+    ## ✅ Step 1: Transcribe Audio Interviews
+    - Use a high-quality speech-to-text model:
+      - [OpenAI Whisper](https://github.com/openaihttps://azure.microsoft.com/en-us/services/cognitive-services/speech-to-text context
+
+    ---
+
+    ## ✅ Step 2: Preprocess Text
+    - Clean transcripts:
+      - Remove filler words
+      - Normalize punctuation
+    - Segment by:
+      - **Survey question triggers**
+      - **Brand character mentions**
+
+    ---
+
+    ## ✅ Step 3: Combine Survey Data
+    - Use survey responses as **metadata**:
+      - Link each interview segment to corresponding survey answers
+      - Helps LLM understand context (e.g., "This person rated Brand A as 'trustworthy' but said X in the interview")
+
+    ---
+
+    ## ✅ Step 4: Use LLM for Sentiment + Thematic Analysis
+
+    ### **A. Sentiment Analysis**
+    - Define **custom sentiment dimensions** relevant to brand characters:
+      - Trustworthiness
+      - Friendliness
+      - Professionalism
+      - Authenticity
+    - Prompt the LLM with **few-shot examples**:
+      - Show examples of text and classification for each dimension
+    - Example output format:
+    ```json
+    {
+      "brand_character": "Brand A",
+      "voice": "Friendly",
+      "sentiment": {
+        "trustworthiness": "positive",
+        "friendliness": "neutral",
+        "professionalism": "negative"
+      },
+      "key_quotes": ["I felt it was too casual for a serious brand."]
+    }
+    """)
+    return
+
+
+@app.cell
+def _(mo):
+    mo.md(r"""
+    # Findings from Foundational Research Report
+
+    ## Brand character
+
+    ### Brand tone (Foundation research delivery v1 for more details) (we need to provide the LLM with definitions of these attributes)
+    - Confident
+    - Progressive
+    - Clear
+    - Intentional
+
+    ### Six CDA brand character personalities
+    - The bank teller: patient, grounded, down-to-earth, knowledgable, stable, steady, balanced, competent
+    - The familiar friend: warm, friendly, approachable, familiar, casual, appreciative, benevolent
+    - The coach: empowering, encouraging, caring, positive, optimistic, guiding, reassuring
+    - The personal assistant: proactive, progressive, cooperative, intentional, deliberate, resourceful, attentive adaptive
+    - The engineer: clear, modest, savvy, plainspoken, straight forward, direct, practical, transparent
+    - The counselor: confident, calm, reliable, dependable, respectable, reassuring, upright
+
+    ### Personality model alternative dimensions (dimensions which were used to defined the six characters)
+    - Approachable: friendly, warm, welcoming
+    - Social-entertaining: humorous, playful, engaging
+    - Social-inclined: eager to converse, talkative, socially oriented
+    - Social assisting: supportive, empathetic, encouraging
+    - Self-conscious: cautious, modest, hesitant
+    - Artifical: robotic, mechanical, lacking human-like warmth
+
+    ## Voice
+    """)
+    return
+
+
+if __name__ == "__main__":
+    app.run()